What is machine learning?
Machine learning is a branch of AI in which software learns patterns from historical data, rather than following hand-written rules, so it can make predictions or classifications about new cases, such as which leads are likely to convert.
Traditional software follows rules a person wrote: if the invoice is over 30 days old, send a reminder. Machine learning finds the rules itself by studying examples. Show it thousands of past jobs with their outcomes, and it learns which features tend to predict a callback, a cancellation or a late payment. Generative AI is one branch of machine learning, focused on producing content.
Many tools you already use include machine learning quietly: spam filtering in Microsoft 365, lead scoring in HubSpot, fraud checks in your payment processor. Building your own model is a bigger step. It needs enough clean, consistent historical data, a clear question to answer, and someone to monitor whether predictions stay accurate as conditions change.
For most firms with 5 to 150 staff, the honest path is to use the machine learning built into existing software before commissioning anything custom. If you do want a custom model, the first job is usually data cleanup, because predictions built on duplicate customers and missing fields will be confidently wrong. Measure accuracy against a simple baseline rule.
Example
For example, a physical therapy clinic could test whether a machine learning model predicts which patients are likely to miss appointments, using past attendance, booking lead time and visit type. Before building anything, the clinic compares it against a simple rule, such as extra reminders for anyone booked more than three weeks out. The details are illustrative.